Grey Scale Skeletonisation with Curvature Sensitive Noise Damping

  • Huaijun Qiu
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


This paper describes a method for curvature dependent skeletonisation in grey-scale images. We commence from a magnetostatic analogy, where the tangential edge flow is intepretted as a current. A vector potential is constructed by integrating the current weighted by inverse distance over the image plane. The skeleton corresponds to the location of valley lines in the vector potential. To damp noise effects we damp the current with an exponential function of the local curvature. In addition, we describe a number of postprocessing steps that can be used to improve the quality of the detected skeletons. In the end, we compare the effects of two alternative ways for noise damping.


Image Plane Vector Potential Synthetic Image Eikonal Equation Symmetry Line 
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  1. 1.
    Arcelli, C., Di Baja, G.S.: A width-independent fast thinning algorithm. IEEE PAMI 7(4), 463–474 (1985)Google Scholar
  2. 2.
    Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen-Dunn, W. (ed.) Models for the perception of speech and visual form, MIT Press, Cambridge (1967)Google Scholar
  3. 3.
    Cross, A.D.J., Hancock, E.R.: Scale space vector fields for symmetry detection. Image and Vision Computing 17, 337–345 (1999)CrossRefGoogle Scholar
  4. 4.
    Harris, C.G., Stephens, M.J.: A combined corner and edge detector. In: Proceedings Fourth Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  5. 5.
    Luo, B., Cross, A.D., Hancock, E.R.: Corner detection via topographic analysis of vector potential. Pattern Recognition Letters 20(6), 635–650 (1999)CrossRefGoogle Scholar
  6. 6.
    Moravec, H.: Obstacle avoidance and navigation in the real world by a seeing robot rover. doctoral dissertation, Robotics Institute, Carnegie Mellon University (1980)Google Scholar
  7. 7.
    Ogniewicz, R.L., Kübler, O.: Hierarchic voronoi skeletons. Pattern Recognition 28(3), 343–359 (1995)CrossRefGoogle Scholar
  8. 8.
    Siddiqi, K., Bouix, S., Tannenbaum, A., Zucker, S.W.: The hamilton-jacobi skeleton. In: International Conference on Computer Vision, pp. 828–834 (1999)Google Scholar
  9. 9.
    Siddiqi, K., Shokoufandeh, A., Dickinson, S.J., Zucker, S.W.: Shock graphs and shape matching. International Journal of Computer Vision 35(1), 13–32 (1999)CrossRefGoogle Scholar
  10. 10.
    Tari, Z.S.G., Shah, J., Pien, H.: Extraction of shape skeletons from grayscale images. Computer Vision and Image Understanding 66, 133–146 (1997)CrossRefGoogle Scholar
  11. 11.
    Tek, H., Stoll, P.A., Kimia, B.B.: Shocks from images: Propagation of orientation elements. In: IEEE CVPR, pp. 839–845 (1997)Google Scholar
  12. 12.
    Torsello, A., Hancock, E.R.: A skeletal measure of 2d shape similarity. In: Arcelli, C., Cordella, L.P., Sanniti di Baja, G. (eds.) IWVF 2001. LNCS, vol. 2059, pp. 260–271. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Huaijun Qiu
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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